Abstract
(Natural Language Processing) NLP techniques such as text classification and topic discovery are very useful in many application areas including information retrieval, knowledge discovery, policy formulation, and decision-making. However, it remains a challenging problem in cases where the categories are unavailable, difficult to differentiate, or are interrelated. Social analysis with human context is an area that can benefit from text classification, as it relies substantially on text data. The focus of this paper is to enhance the classification of text according to the UN's Sustainable Development Goals (SDGs) by collecting and combining intelligence from multiple models. Combinatorial Fusion Analysis (CFA), a system fusion paradigm using a rank-score characteristic (RSC) function and cognitive diversity (CD), has been used to enhance classifier methods by combining a set of relatively good and mutually diverse classification models. We use a generative AI model to generate synthetic data for model training and then apply CFA to this classification task. The CFA technique achieves 96.73% performance, outperforming the best individual model. We compare the outcomes with those obtained from human domain experts. It is demonstrated that combining intelligence from multiple ML/AI models using CFA and getting input from human experts can, not only complement, but also enhance each other.
Abstract (translated)
自然语言处理（NLP）技术，如文本分类和主题发现，在信息检索、知识发现、政策制定和决策等许多应用领域都非常有用。然而，在类别缺失、难以区分或相互关联的情况下，这仍然是一个具有挑战性的问题。社会分析中包含的人类上下文可以从文本分类中受益，因为它很大程度上依赖于文本数据。本文的重点是通过收集并整合来自多个模型的智能来增强根据联合国可持续发展目标（SDGs）对文本进行分类的能力。 组合融合分析（CFA），一种使用排名分数特性（RSC）函数和认知多样性（CD）的系统融合范式，已被用于通过结合一组相对良好且相互多样化的分类模型来改进分类方法。我们利用生成型AI模型生成合成数据以供模型训练，并将CFA应用于该分类任务。采用CFA技术达到了96.73%的表现水平，超过了单一最佳模型的成绩。 我们将结果与人类领域专家的结果进行了比较，证明了使用CFA整合多个机器学习/人工智能模型的智能并结合人类专家的意见不仅能够互补，还能相互增强彼此的能力。
URL
https://arxiv.org/abs/2602.11168
